Small fix figure numbers and clear import

1_Earlier_measurements_images.ipynb

@@ -23,9 +23,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# importing the necessary libraries for data visualization and numerical operations\n",
-    "# matplotlib.pyplot is used for plotting graphs, and numpy is used for handling numerical data efficiently\n",
-    "\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np"
    ]
@@ -168,7 +165,8 @@
    "id": "5772bcf6-3ee6-49fe-9310-71cdbd09273a",
    "metadata": {},
    "source": [
-    "### Figure: Seasonal variation based on earlier measurement results"
+    "### Figure 1.1\n",
+    "Seasonal variation based on earlier measurement results"
    ]
   },
   {

2_Vostok_measurements_images.ipynb

@@ -336,7 +336,7 @@
    "id": "e5a58975-053a-4162-bccb-f0dbef39b0ed",
    "metadata": {},
    "source": [
-    "### Figure: Seasonal variation (new data) for different years"
+    "### Figure 1.2: Seasonal variation (new data) for different years"
    ]
   },
   {
@@ -463,7 +463,7 @@
    "id": "1b65fb2a-8f3d-4d23-a8a2-c4c7f031c910",
    "metadata": {},
    "source": [
-    "### Figure: Diurnal-Seasonal Diagram"
+    "### Figure 1.3: Diurnal-Seasonal Diagram"
    ]
   },
   {
@@ -912,7 +912,7 @@
    "id": "f0590fff-4817-440f-9550-d4438b742769",
    "metadata": {},
    "source": [
-    "### Figure: source vs adjustment PG for new and earlier Vostok datasets"
+    "### Figure 1.5: source vs adjustment PG for new and earlier Vostok datasets"
    ]
   },
   {

3_WRF_T2_images.ipynb

@@ -23,9 +23,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# importing the necessary libraries for data visualization and numerical operations\n",
-    "# matplotlib.pyplot is used for plotting graphs, and numpy is used for handling numerical data efficiently\n",
-    "\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np"
    ]

4_IP_simulations_temporal_images.ipynb

@@ -18,19 +18,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 12,
    "id": "e6e8b28e-203f-4c4b-907f-fe6183e5d331",
    "metadata": {},
    "outputs": [],
    "source": [
     "import datetime as dt\n",
     "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.transforms as tf\n",
     "import numpy as np\n",
-    "import pandas as pd\n",
     "import scipy.stats as st\n",
-    "from matplotlib import cm, colormaps, colors, transforms"
+    "\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
@@ -133,7 +131,7 @@
    "id": "ae294872-6a91-44d9-8f26-17ab169a9c30",
    "metadata": {},
    "source": [
-    "### Figure 1"
+    "### Figure 2.1"
    ]
   },
   {
@@ -296,7 +294,7 @@
    "id": "6d2b0559-ca51-4dd7-98e8-af08fb402886",
    "metadata": {},
    "source": [
-    "### Figure 5"
+    "### Figure 2.5"
    ]
   },
   {

5_IP_simulations_spatial_images.ipynb

@@ -31,23 +31,18 @@
    "source": [
     "import datetime as dt\n",
     "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.transforms as tf\n",
     "import numpy as np\n",
-    "import pandas as pd\n",
     "import scipy.stats as st\n",
-    "from matplotlib import cm, colormaps, colors, transforms\n",
     "\n",
-    "from functools import cache\n",
-    "import cartopy.crs as ccrs"
+    "import cartopy.crs as ccrs\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import cm, colormaps, colors, transforms"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "83163834-de47-4f28-8add-768c7b76e1d3",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
+   "metadata": {},
    "source": [
     "### Helper functions, variables and classes"
    ]
@@ -521,7 +516,7 @@
     "tags": []
    },
    "source": [
-    "### Figure 4"
+    "### Figure 2.4"
    ]
   },
   {
@@ -655,7 +650,7 @@
    "id": "5ed8b0d4-c9d0-4a9a-ab87-263350eeed15",
    "metadata": {},
    "source": [
-    "### Figure 2"
+    "### Figure 2.2"
    ]
   },
   {
@@ -1046,7 +1041,7 @@
    "id": "fb649ed7-f596-4156-b861-cb72461523ce",
    "metadata": {},
    "source": [
-    "### Figure 6"
+    "### Figure 2.6"
    ]
   },
   {

readme.md

@@ -1,6 +1,6 @@
 # Short Description of the Scripts
 
-> **_Note:_** For analysis, we use simulation data of the ionospheric potential through climate models. Since these data are very large (around 350 GB), we only upload preprocessed lower-dimensional data (a few tens of MB) to the repository. Data preparation is possible using the script `0_prepare_data.ipynb`, but this would require downloading large files from https://eee.ipfran.ru/files/seasonal-variation-2024/.
+> **_Note:_** For analysis, we use simulation data of the ionospheric potential through climate models. Since these data are very large (around 350 Gb), we only upload preprocessed lower-dimensional data (around 20 Mb) to the repository. Data preparation is possible using the script `0_prepare_data.ipynb`, but this would require downloading large files from https://eee.ipfran.ru/files/seasonal-variation-2024/.
 
 * `1_Earlier_measurements_images.ipynb` plots seasonal variations from external sources
 * `2_Vostok_measurements_images.ipynb` plots seasonal variations and seasonal-dirunal diagram using new and early Vostok PG measurements
@@ -66,7 +66,7 @@ For clarity, we also present slices of this diurnal-seasonal diagram at 3, 9, 15
 
 > **_Note:_**  Renaming the axes of the multi-index resulting from grouping (`sd_df.index.set_names(['hour', 'month'], inplace=True)`) is not necessary for the code and can be commented out; however, it may be convenient for further work with the diurnal-seasonal dataframe `sd_df`.
 
-### Figure 1.4
+### Figure 1.5
 #### Removal of field anomalies associated with meteorological parameters
 
 First, we load the meteorological datasets (`temp_df`, `wind_df`, `pressure_df`), averaged by days (`vostok_daily_temp`, `vostok_daily_wind`, `vostok_daily_pressure_mm_hg`). For further analysis, we use the `meteo_df` dataframe, which is created by merging the dataframe with daily average potential gradient values (`daily_df`).
@@ -86,7 +86,7 @@ This script calculates the seasonal variation of the 2m-level temperature (T2m)
 
 In the script, temperature data averaged by longitude and by month are loaded (see data description below) from `WRF_T2_MONxLAT.npy`.
 
-Next, the temperature is averaged across latitude bands 20° S–20° N, 30° S–30° N, 40° S–40° N, and 50° S–50° N. The averaging takes into account the latitudinal area factor; degree cells at higher latitudes are summed with a diminishing coefficient. The results of the averaging (seasonal temperature variation in the specified latitude band) are displayed on a figure consisting of four panels.
+Next, the temperature is averaged across latitude bands 20° S–20° N, 30° S–30° N, 40° S–40° N, and 50° S–50° N. The averaging takes into account the latitudinal area factor; degree cells at higher latitudes are summed with a diminishing coefficient. The results of the averaging (seasonal temperature variation in the specified latitude band) are displayed on a figure 1.4, 2.3 consisting of four panels.
 
 ## Script `4_IP_simulations_temporal_images.ipynb`
 ...